Max Pooling Test CPU vs GPU

gistfile1.txt

val height = 8
    val width = 8
    val depth = 3

    var img = Nd4j.ones(1, depth, height, width)

     for (i <- 0 until depth) {
        for (j <- 0 until height) {
          for (k <- 0 until width) {
           img.put(Array(NDArrayIndex.point(0), NDArrayIndex.point(i), NDArrayIndex.point(j), NDArrayIndex.point(k)), j+k)
          }
        }
      }

  val builder_pooling = new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX)
    .name("max_pooling")
    .kernelSize(2, 2)
    .stride(1, 1)
    .build

    private def convInit(name: String, in: Int, out: Int, kernel: Array[Int], stride: Array[Int], pad: Array[Int], bias: Double): ConvolutionLayer =
      return new ConvolutionLayer.Builder(kernel, stride, pad).name(name).nIn(in).nOut(out).biasInit(bias).activation(Activation.RELU).build


    val conf: MultiLayerConfiguration = new NeuralNetConfiguration.Builder()
      .iterations(1)
      .weightInit(WeightInit.ONES)
      .list
      .layer(0, convInit("cnn1", depth, 1, Array[Int](3, 3), Array[Int](1, 1), Array[Int](0, 0), 0))
      .layer(1, builder_pooling)
      .setInputType(InputType.convolutional(height, width, depth)).build


    val net = new MultiLayerNetwork(conf)

    net.init()

    val output = net.output(img)